Skip to page content
SAVRN
Search Plan a project

Open-weight model · Text generation

VeriLoop-E2-Uncensored-W4A16

by Libo bowmanslayer/VeriLoop-E2-Uncensored-W4A16

VeriLoop-E2-Uncensored-W4A16 is an open-weight model for text generation from Libo, released under Apache License 2.0. It has 6.3B parameters and a 262,144-token context. At 16-bit it needs about 15.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

Community abliteration and quantization of tsinghua-sigs-robot-lab/VeriLoop-E2, fixed revision 9379d199adcc44acdf836558037163878e3a37ae. This is not an official Tsinghua release.

Parameters6.3B
Context262,144
Weights19.0 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve VeriLoop-E2-Uncensored-W4A16 (6.3B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 12.6 GB 15.1 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 6.3 GB 7.5 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 3.1 GB 3.8 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 22, 2026.

VeriLoop-E2-Uncensored-W4A16 on every accelerator the SAVRN Index prices, at every precision

Model Card

By Libo, published under apache-2.0, revision 118974236c59.

Community abliteration and quantization of tsinghua-sigs-robot-lab/VeriLoop-E2, fixed revision 9379d199adcc44acdf836558037163878e3a37ae. This is not an official Tsinghua release. Model architecture is Qwen35ForConditionalGeneration; the upstream names the base Qwen3.8-27B. AutoRound W4A16, symmetric group 128, 256 public Pile calibration samples, 512 tokens, 200 iterations, batch 4. Eligible weights are INT4; small hybrid projections, activations and retained weights are BF16. All source tensors are represented; the 333 vision tensors are preserved exactly. A bounded six-candidate Heretic 1.4.0 search selected per-layer strength 1.2; 96 of 128 targeted residual-output matrices changed and…

Read Libo's full model card

Safety refusals have been largely removed from this model. It will comply with requests the original model refuses, including offensive, dangerous, or jurisdiction-illegal content. Marker absence in our probes is not proof of complete removal, and no content filter or guardrail remains. You are solely responsible for how you deploy it and for compliance with applicable law. Read §② Safety refusals largely removed below before use.

本模型的安全拒绝已被大幅移除。 它会执行原模型拒绝的请求,包括冒犯、危险、 或所在司法辖区违法的内容。探针中标记词消失不等于移除穷尽,且不再有任何内容过滤 或护栏。部署方式与合规责任完全由使用者自负。使用前请先阅读正文 §② 安全拒绝大幅移除。

Community abliteration and quantization of tsinghua-sigs-robot-lab/VeriLoop-E2, fixed revision 9379d199adcc44acdf836558037163878e3a37ae. This is not an official Tsinghua release. Model architecture is Qwen3_5ForConditionalGeneration; the upstream names the base Qwen3.8-27B.

What is included

AutoRound W4A16, symmetric group 128, 256 public Pile calibration samples, 512 tokens, 200 iterations, batch 4. Eligible weights are INT4; small hybrid projections, activations and retained weights are BF16. All source tensors are represented; the 333 vision tensors are preserved exactly.

A bounded six-candidate Heretic 1.4.0 search selected per-layer strength 1.2; 96 of 128 targeted residual-output matrices changed and 32 zero updates remained unchanged. The small selection set improved from 16/16 to 4/16 refusal markers, missing its original ≤1 threshold. This is a reduced-refusal candidate, not a claim of complete removal. The search and modification recipe is not included in this release.

Local paired evaluation

Same frozen 1,514 questions, seed 20260816, thinking enabled with the original template and publisher raw-content protocol, greedy decoding, max output 32,768 tokens. These are our legacy sampled/custom scorers, not official leaderboard results. IFEval uses a partial custom rule implementation. See EVALUATION.md and evaluation.json for details and validity flags.

Task n Original w4a16 Ablated w4a16 Delta pp
mmlu 150 84.00% 76.00% -8.00
cmmlu 150 84.67% 75.33% -9.33
mmlu_pro 150 78.67% 70.67% -8.00
ceval 150 84.00% 76.00% -8.00
arc 150 80.67% 85.33% +4.67
truthfulqa 150 58.67% 71.33% +12.67
gsm8k 100 95.00% 88.00% -7.00
math500 100 75.00% 73.00% -2.00
bbh 150 75.33% 65.33% -10.00
humaneval 164 89.63% 89.63% +0.00
ifeval 100 76.00% 69.00% -7.00

Overall original → ablated: 80.05% → 76.42%, difference -3.63 pp; approximate paired 95% CI [-5.894, -1.371] pp. Empty responses: [9, 8]; length-limited responses: [9, 8]. Both stay in the denominator. Empty-rate validity gate: False.

On 100 separate refusal probes (thinking disabled, 2048-token budget), opening-30-word marker counts were 98/100 → 0/100. Marker absence is not proof of semantic compliance. Truncation/budget hits: [1, 9]; validity: [True, True]. The first 16 probes overlap candidate selection; see the 84-item held-out subset in evaluation.json when available.

Loading

vllm serve /path/to/VeriLoop-E2-Uncensored-W4A16 --tensor-parallel-size 2 --generation-config vllm

The measured runtime was vLLM 0.29.0 with a local FA2/FP8-KV plugin on dual RTX 3090. That local setup requires its plugin-specific library environment variables; this repository does not bundle the plugin. The command above is a starting point for a compatible installed runtime, not a tested stock-vLLM support matrix. No remote custom model code is required. Vision weights are retained exactly, but visual inference is not validated in this evaluation.

Scope and limitations

Use the repository-shipped template and publisher raw-content serving protocol. Do not add a Qwen3 reasoning parser: some completed direct answers omit a closing think tag and that parser classifies them entirely as hidden reasoning. Removing it recovered all 33 initial missing-answer cases; streaming/nonstreaming checks passed. Thinking remains enabled. For evaluation, final content is taken after </think> when present; normally stopped direct content is retained, while unclosed length-limited output is not scored as an answer.

In llama.cpp content-only mode, remove an echoed opening <think> generation-prefix tag before applying answer extraction. It is protocol framing, not answer text. The GGUF preflight was restarted after verifying this correction and the template override; it is excluded from the reported scores.

The author's private VeriLoop Harness is not included or reproduced. Their SWE/Terminal/DeepSWE scores are not scores for these weights. This evaluation does not establish long-context, tool-use, vision, agentic performance, or production suitability. Reduced refusal can affect inappropriate-content handling; applications need their own behavior controls. Full semantic refusal review and comprehensive capability equivalence have not been established.

Apache-2.0 is retained from the fixed upstream source; see LICENSE. Source training belongs to its original authors. Community changes are quantization and the described abliteration. See release.json and SHA256SUMS for provenance and integrity.

② Safety refusals largely removed / 安全拒绝大幅移除

This is the point of the model. Read before downloading. 这是模型的核心属性,下载前请务必阅读。

The abliteration removes most of the refusal behaviour trained into VeriLoop-E2. On 100 refusal probes (thinking disabled, 2048-token budget), first-30-word refusal markers fell 98/100 → 0/100. Broader lexical lists still detect refusal markers in many long answers, so refusal removal is not proven exhaustive, and marker absence is not a semantic compliance judgment.

消融移除了 VeriLoop-E2 训练进去的大部分拒绝行为。100 条拒绝探针(思考关闭、2048 token 预算) 上,前 30 词拒绝标记从 98/100 降到 0/100。更宽的词表仍能在不少长回答里检出拒绝标记, 因此移除未证明穷尽,标记消失也不等于语义层面会照做。

Consequences you accept / 你正在接受的后果:

  • Content the original refuses, including offensive / dangerous / jurisdiction-illegal. 会产生原模型拒绝的内容,包括冒犯 / 危险 / 所在地违法
  • No content filter, no safe-completion, no guardrail. 无内容过滤、无 safe-completion、无护栏
  • Knowledge/reasoning capability measurably dropped vs the unmodified W4A16 (80.05% → 76.42% overall; see the table above). 相对未消融 W4A16,知识/推理能力有可测下降(综合 80.05% → 76.42%,见上表)。

Not intended for / 不适用于:

  • Third-party or public-facing deployment without your own safety layer / 未搭建自己的安全层就部署给第三方或面向公众
  • Anyone under legal age in their jurisdiction / 未达所在司法辖区法定年龄的人
  • Any use prohibited by upstream acceptable-use policies / 上游可接受使用政策禁止的任何用途

Intended for / 适用于: local inference and research, by people who understand the above and take responsibility for it / 本地推理与研究,由理解以上内容并为其 负责的用户使用。

③ No warranty; responsibility rests with the user / 无担保;责任在用户

Provided "AS IS", without warranty of any kind, express or implied.

  • The publisher does not endorse, recommend, or condone any particular use.
  • No representation outputs are accurate, lawful, or fit for any purpose.
  • You are solely responsible for what you generate, how you deploy, and compliance with all laws applicable to you.
  • To the maximum extent permitted by law, no liability for any claim, damage, or other liability arising from the model or its use.

按"现状"提供,不提供任何担保。发布者不背书、不推荐,不对输出的准确性/合法性/ 用途适用性作声明。你独自承担全部责任。法律允许最大范围内不承担任何责任。

Downloading these files means you accept the above. If you do not, do not download.

下载本仓文件即表示接受以上内容。若不接受,请勿下载。

The author is not a lawyer; the above is not legal advice. 作者不是律师;以上不构成法律意见。

Complete measurement records and historical comparison

See all six result columns, test standards and limitations, all 9,084 per-item metric records, question IDs and hashes, and 400 refusal-probe metric records. No full-precision baseline was measured. Historical results are not a strictly identical-runtime comparison.

Configuration

Architecture
Qwen3_5ForConditionalGeneration
Context length (tokens)
262,144
Layers
64
Hidden size
5,120
Feed-forward size
17,408
Attention heads
24
Key/value heads
4
Head dimension
256
Vocabulary size
248,320
Model type
qwen3_5
Quantization
auto-round

Identity and Version

Repository
bowmanslayer/VeriLoop-E2-Uncensored-W4A16
Publisher
Libo
Task
Text generation
Modality
Text
Library
transformers
Parameters
6.3B parameters
Languages
Not stated by the source
Revision
118974236c59149f2af3862f0966b46b8dc24e83
First published
2026-10-12
Last updated
2026-10-12

Files and Weights

38 files, 19.0 GB in total. The weights are 11 files totalling 19.0 GB in safetensors.

Weights11 files · 19.0 GB
Configuration18 files · 3.7 MB
Tokenizer2 files · 20.0 MB
Documentation4 files · 35.2 KB
Other2 files · 20.8 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00010.safetensorsWeights2.1 GB 737651d327b6
model-00002-of-00010.safetensorsWeights2.1 GB a6a73491ef97
model-00003-of-00010.safetensorsWeights2.1 GB 6a512cb76d65
model-00004-of-00010.safetensorsWeights2.1 GB f05c776746a2
model-00005-of-00010.safetensorsWeights2.1 GB 630407b320fe
model-00006-of-00010.safetensorsWeights2.1 GB efafee3cee4c
model-00007-of-00010.safetensorsWeights800.6 MB f26e2f4b123c
model-00008-of-00010.safetensorsWeights2.5 GB 1e22bff91476
model-00009-of-00010.safetensorsWeights10.3 KB 1c00c437d08b
model-00010-of-00010.safetensorsWeights2.5 GB 6866cf8adccc
model_extra_tensors.safetensorsWeights313.0 MB 05071fbf80f6
config.jsonConfiguration16.3 KB —
evaluation-adapter.pyConfiguration4.2 KB —
evaluation-item-alignment.jsonConfiguration1.5 KB —
evaluation-items.jsonConfiguration3.0 MB —
evaluation-legacy-scorer.pyConfiguration31.6 KB —
evaluation-marker-definitions.jsonConfiguration2.0 KB —
evaluation-protocol.pyConfiguration840 B —
evaluation-question-indices.jsonConfiguration206.7 KB —
evaluation-questions-manifest.jsonConfiguration344 B —
evaluation-refusal-items.jsonConfiguration178.7 KB —
evaluation-runtime-versions.jsonConfiguration1.2 KB —
evaluation.jsonConfiguration13.5 KB —
generation_config.jsonConfiguration202 B —
model.safetensors.index.jsonConfiguration196.2 KB —
preprocessor_config.jsonConfiguration443 B —
processor_config.jsonConfiguration1.2 KB —
quantization_config.jsonConfiguration11.0 KB —
release.jsonConfiguration4.7 KB —
EVALUATION.mdDocumentation5.9 KB —
HISTORICAL-COMPARISON.mdDocumentation8.3 KB —
LICENSEDocumentation11.3 KB —
README.mdDocumentation9.6 KB —
SHA256SUMSOther3.3 KB —
chat_template.jinjaOther17.5 KB —
.gitattributesRepository1.6 KB —
tokenizer.jsonTokenizer20.0 MB 06b9509352d2
tokenizer_config.jsonTokenizer1.2 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
19.0 GB
Download from Libo

Released by Libo through its official repository on Hugging Face. Read the license.

Built From

  • Derived from tsinghua-sigs-robot-lab/VeriLoop-E2
  • Quantized from tsinghua-sigs-robot-lab/VeriLoop-E2

Memory Requirements

PrecisionWeights in memory
As published19.0 GB
16-bit12.6 GB
8-bit6.3 GB
4-bit3.1 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About VeriLoop-E2-Uncensored-W4A16

How much GPU memory does VeriLoop-E2-Uncensored-W4A16 need?

About 15.1 GB at 16-bit and 3.8 GB at 4-bit: the weights (6.3B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run VeriLoop-E2-Uncensored-W4A16 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use VeriLoop-E2-Uncensored-W4A16 commercially?

Yes. VeriLoop-E2-Uncensored-W4A16 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

What is VeriLoop-E2-Uncensored-W4A16's context length?

262,144 tokens, from the maximum position embeddings in its published configuration.

Similar Models

Model · Text generation

Vigyan-1.5B-4x-MoE

Shreyansh singh

Vigyan-1.5B 4× MoE is an experimental Sparse Mixture of Experts model upcycled from 4 specialized domain LoRA adapters (Science, Technology, Engineering, Mathematics). It was assembled to evaluate whether post-hoc expert stitching on compact language models ($\le 3\text{B}$) can deliver multi-domain specialization at single-expert inference latency with a sub-1GB RAM footprint. A pre-quantized standalone GGUF (vigyan-1.5b-4x-moe.Q4KM.gguf) is available directly within this repository for instantaneous local execution on CPU or mobile. Test this model on Google Colab with an embedded Gradio chat interface: - shreyansh12183/vigyan-1.5b-adapter-science…

Open weights cc-by-nc-4.0 6.4B parameters 131,072 tokens

Model · Text generation

Ornith-1.5-9B-NVFP4

Ornith

Chirp Chirp! We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For…

Open weights mit 6.7B parameters 262,144 tokens transformers

A Llama-2-7b-chat checkpoint compressed with SVD-LLM to 60.0% of dense parameters, then edited by 10 of 10 rounds of iterative parameter-neutral swap selected by the gapiter rule (up to 0.1% of dense parameters per round; the full run's budget is 1.0%). This is a research artifact from a study of how SVD compression damages safety behaviour and which component-selection rule best repairs it. It is one cell of a grid over selection rules and budgets; it is not a general-purpose chat model. This checkpoint exists to measure safety/utility trade-offs under compression. Several arms in the grid are deliberately safety-degraded relative to Llama-2-7b-chat: compression alone raises attack-success…

Open weights llama2 6.7B parameters 4,096 tokens transformers

A Llama-2-7b-chat checkpoint compressed with SVD-LLM to 60.0% of dense parameters, then given a 0.0% parameter budget of restored SVD components selected by the unknown rule. This is a research artifact from a study of how SVD compression damages safety behaviour and which component-selection rule best repairs it. It is one cell of a grid over selection rules and budgets; it is not a general-purpose chat model. This checkpoint exists to measure safety/utility trade-offs under compression. Several arms in the grid are deliberately safety-degraded relative to Llama-2-7b-chat: compression alone raises attack-success rate, and the point of the study is to quantify that and test recovery. Treat…

Open weights llama2 6.7B parameters 4,096 tokens transformers

Model · Text generation

Llama-2-7b-hf

Meta Llama

Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the website and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion…

Access requested at publisher llama2 6.7B parameters transformers

Model · Text generation

finma-7b-nlp

The Fin AI

FinMA-7B-NLP is a financial large language model (LLM) developed as part of the PIXIU project. It is designed to understand complex financial language and concepts, and is fine-tuned to follow natural language instructions, enhancing its performance in downstream financial tasks. Specifically, FinMA-7B-NLP is trained only on the NLP tasks of the PIXIU dataset, making it specialized for tasks such as sentiment analysis, news headline classification, named entity recognition, and question answering. In addition to FinMA-7B-NLP, the PIXIU project includes two other models: FinMA-7B-full and FinMA-30B. You can use the FinMA-7B-NLP model in your Python project with the Hugging Face Transformers…

Access requested at publisher mit 6.7B parameters transformers